.. _home:

=================
Welcome to sktime
=================

A unified framework for machine learning with time series.

.. topic:: Register as a user!

    Prioritized bugfixes, shape the tech roadmap and governance policy.
    `Register here <https://forms.gle/eVuzrCjKDRupxawL7>`_


Mission
=======

.. include:: about/_mission.rst

Features
========

* **unified API** for ML/AI with time series, for model building, fitting, application, and validation
* **support for a variety of learning tasks** including :ref:`forecasting <forecasting_ref>`, :ref:`time series classification <classification_ref>`, :ref:`regression <regression_ref>`, :ref:`clustering <clustering_ref>`.
* **composite model building**, including pipelines with transformations, ensembles, tuning, reduction
* interactive user experience with **scikit-learn like interface conventions**

Technical specification
=======================

* In-memory computation of a single machine, no distributed computing
* Medium-sized data in pandas and NumPy based containers
* Modular, principled and object-oriented API
* Using interactive Python interpreter, no command-line interface or graphical user interface

Contents
========

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   get_started
   users
   installation
   estimator_overview
   api_reference
   get_involved
   developers
   about
   examples

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        Get Started

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        Get started using ``sktime`` quickly.

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            Get Started

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        Documentation

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        User documentation.

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            Documentation

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        Installation

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        Installation Guide.

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            Installation

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        Estimator Overview

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        Overview of all estimators in ``sktime``.

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            Estimator Overview

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        API Reference

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        Understand sktime's API.

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            API Reference

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        Get Involved

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        Find out how you can contribute.

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            Get Involved

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        Developers

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        Information for developers.

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            Developers

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        About

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        Learn more about ``sktime``.

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            Learn More
